Lead Analyst, Data Operations

In this role at Facet, you will lead efforts to develop and equip our teams with the information they need to ensure business and financial information is reliable, up-to-date, and maintained consistently. You will be tasked with ensuring the definitions and logic behind specific data elements are maintained and distributed as needed. You will join the Data Operations Team within the Business Operations department, which is responsible for modeling and understanding the operational and financial efficacy of the organization. You will frequently partner with the FP&A team to help fuel the financial model, KPIs, and monthly reporting output. You will have the opportunity to become a highly visible leader at Facet,

• by promoting scalable reporting and analytic strategies
• building a foundation of highly standardized data requirements and models
• evaluating new tools and resources necessary to enable self-service reporting and analysis
• using your analytics and communication abilities to drive evidence-based change in Facet’s strategy.

Finally, governance around the data is key to success in this role. You must be able to assist in tracking anomalies and understanding the root cause ( automated or manual inputs, pipelines and feeds or modelling). You will need to work cross-functionally to troubleshoot and resolve these issues to confirm data integrity.. This role will report directly to the Senior Manager of Data Operations .

This role must fully understand our data and how its transformed from the raw source data, through the modeling layer and finally at the query/presentation layers. The Business reasons for the logic behind the data and why specific metrics, tracking views KPIs and analysis must be paramount to this person who fills this role.

Day-To-Day Responsibilities:

• Drive improvement in Facet’s business. As a Lead Data Analyst, you will be responsible for leveraging research and analytics techniques to discover ways to improve Facet’s business. You will proactively identify opportunities and prioritize your work to help Facet achieve its business priorities. You will work directly with your teammates across the business to disseminate insights and drive change in strategy.
• Lead self-service KPI, reporting and analysis via various BI Tools (eg. Tableau, Salesforce). You will partner with various teams (including Planning, Investment Operations, Billing, Membership Experience, Support, Sales, Finance) to plan self-service reports and analyses, work with the Data Engineering team to ensure we collect and replicate all necessary measurements in our data warehouse, model and analyze data, then present results and build reporting to drive improvement in Facet’s operations.
• A large focus on supporting views and data sets to fuel the FP&A function in corporate forecasting and decision-making.
• Manage Ad Hoc requests and views into discrete initiatives and projects to ensure teams have visibility into performance and success while providing insights to allow teams to be agile and pivot based on results.
• Collaborate on analytics engineering projects and assist in managing and maintaining relevant data models
• You will understand the findings of our Data Science department and ensure operational views for teams to adhere to those suggestions are given along with feedback on results to ensure the correct course is being followed or if adjustments should be made. These results are to be taken back to the appropriate business units.
• Ensure health and accuracy of data (following definitions and expected patterns) via governance. Working with partners to alert them to this and confirm resolution.
• This role should have insight and feed into the modelling of the data while keeping analytical needs in mind along with ensuring the data is set in a way to optimize upstream querying and presentation layers.

Requirements

• 6+ years of experience in a BI or analytics role, with at least 3 years of experience working with various sets of data from multiple sources.
• Proficient at sales forecasting and multi-touch, weighted attribution modeling.
• Experience with statistics and problem solving
• Understanding of warehouse-driven customer data platform concepts, including the development of identity resolution models - creating common identifiers to join customer data across many data sources and produce a single source-of-truth record of a customer’s history and current state.
• Proficiency with statistical techniques including regression, classification, clustering, and dimensionality reduction.
• A master of SQL and Python.
• Experience collaborating with other analysts, data engineers, and data scientists using Git.
• Strong experience working with an enterprise data warehouse and dbt.
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